The official implementation of PHASE.
Accurate and individualized human head models are becoming increasingly important for electromagnetic (EM) simulations. These simulations depend on precise anatomical representations to realistically model electric and magnetic field distributions, particularly when evaluating Specific Absorption Rate (SAR) within safety guidelines. We introduce Personalized Head-based Automatic Simulation for EM properties (PHASE), an automated open-source toolbox that generates high-resolution, patient-specific head models for EM simulations using paired T1-weighted (T1w) magnetic resonance imaging (MRI) and computed tomography (CT) scans with 13 tissue labels. To evaluate the performance of PHASE models, we conduct semi-automated segmentation and EM simulations on 15 real human patients, serving as the gold standard reference. The PHASE model achieved comparable global SAR and localized SAR averaged over 10 grams of tissue (SAR-10g), demonstrating its potential as a promising tool for generating large-scale human model datasets in the future.
SLANT brain segmentation [1] is applied to segment detailed brain
region. Refer to SLANT
to run the trained model on your T1w MRI volume and get the segmented
.nii.gz final results.
A brain mapping from 133 anatomical labels to 8 tissue groups with distinct electrical properties is applied to the segmented brain. Run:
python SLANT_label_mapping.py --input_dir your/input/dict --output_dir your/output/dict --mapping_file braincolor_hierarchy_STAPLE.txt
SimNIBS [2] and GRACE [3] are used to segment and fill in the other parts of the human head.
With segmented brain from SLANT, other tissues from SimNIBS and GRACE, bones from registered CT, run to combine and perform correction:
python combination.py
.nii to .rawA script transforming .nii file to .raw
file which is importable for most simulation software are provided.
python nii_to_raw.py
An example of a header .txt file needed for
.raw when importing is provided. The grid extent and
spatial steps need to be refined to your models.
[1] Huo, Yuankai, et al. “3D whole brain segmentation using spatially
localized atlas network tiles.” NeuroImage 194 (2019): 105-119.
[2] Puonti, Oula, et al. “Accurate and robust whole-head segmentation
from magnetic resonance images for individualized head modeling.”
Neuroimage 219 (2020): 117044.
[3] Stolte, Skylar E., et al. “Precise and rapid whole-head segmentation
from magnetic resonance images of older adults using deep learning.”
Imaging Neuroscience 2 (2024): 1-21.
For any questions or discussion, email us.